Abstract
Natural frequency spectrum is scared resource; the efficient use of it can only accommodate the need of future computing world.
But efficient use of it is not possible within the existing system, where the allocation of spectrum is done based on fixed spectrum
access (FSA) policy. Many survey shows that it leads to under use of spectrum. For efficient utilization of spectrum innovative
techniques is needed. using Dynamic spectrum access (DSA) policy we can exploiting the available spectrum, For given purpose
Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing opportunistic usage of the
frequency bands that are not heavily occupied by licensed users. This paper presents the study of different spectrum sensing
techniques of cognitive radio networks. As we know Cognitive radio is a form of wireless communication where radio transceiver
intelligently detects which spectrums are free which are not. After this it occupies the vacant one while avoiding busy one
spectrum. Cognitive radios promote open spectrum allocation which is a clear departure from traditional command and control
allocation schemes for radio spectrum usage. In short, they allow the formation of “infrastructure-less” collaborative network
clusters—cognitive radio networks. However, how to detect free spectrum we have to use the spectrum sensing techniques, here
we are describing all the spectrum sensing techniques and Finally concluded that cooperative sensing is better than NonCooperative sensing for primary user (PU) signal with low SNR value.
Keywords- CRN, FSA, PU, DSA, SU, SNR..

I.

INTRODUCTION

Wireless technology will be the backbone of the future computing world one in which a large number of
communicators, mobile devices and sensors are connected to the global Internet and serve as the basic block for many
stirring new classes of applications. As we know that natural frequency spectrum is scared resource, the efficient use
of it can only accommodate the spectrum demand of future computing world. The existing fixed spectrum access
(FSA) policy is not suitable for it as it uses spectrum in very inefficient way. Graph based on Resent survey regarding
partial use of spectrum in FSA scheme is show below in Figure1.
Finding of this serve suggest that for efficient utilization of spectrum innovative techniques is needed. One can
offer new ways of exploiting the available spectrum by using Dynamic spectrum access (DSA) policy. For given
purpose Cognitive radio arises to be a tempting solution to the spectral congestion problem by introducing
opportunistic usage of the frequency bands that are not heavily occupied by licensed users as we can see in figure 1.
Now A Cognitive radio is a system that senses its operational electromagnetic environment and can dynamically
and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput,
mitigate interference, facilitate interoperability, access secondary markets.” [1]
The rest of the paper is organized as follows: Section II presents the classification of spectrum sensing technique.
Section III describes our proposed analysis. Section IV concludes the paper.

The Functions of Cognitive Radio
Spectrum sensing and Analysing: Determine which portion of the spectrum is available and detect the presence
of licensed users when a user operates in a licensed band.



Spectrum management and Handoff: It selects the best available channel (frequency) for communication.



Spectrum sharing and Allocation: It coordinates fair spectrum access to this channel with other users.



Spectrum mobility: Vacate the channel when a licensed user is detected while still maintaining seamless
communication requirements during the transition to a better piece of spectrum.



Approaches for accessing Licensed Spectrum[Figure2]
Three main approaches have been developed for cognitive radio, regarding the way a secondary user accesses the
licensed spectrum:


Through opportunistic spectrum access (OSA), also known as interweave scheme, according to which a
secondary user accesses a frequency band only when it is detected not being used by the primary users [14].



Through spectrum sharing (SS), also known as underlay scheme, based on which the secondary users coexist
with the primary users under the condition of protecting the latter from harmful interference [15-16]



Recently, a third hybrid approach was proposed, aiming to increase the throughput of the two aforementioned
schemes, in which the secondary users initially sense for the status (active/idle) of a frequency band (as in the
OSA) and adapt their transmit power based on the decision made by spectrum sensing, to avoid causing
harmful interference (as in SS) [19].

Whatever the approach is, it is clear that spectrum sensing is the key for all of them. Thus we can say that without
highly accurate and accomplished spectrum technique it is very difficult to make competent Cognitive radio system.
In this paper our discussion will revolve around different spectrum sensing techniques and their advantages as well
as disadvantages.

II. CLASSIFICATION OF SPECTRUM SENSING TECHNIQUE
We can broadly divide spectrum sensing techniques under two categories.
1. Cooperative Detection Technique.
2. Non-cooperative Detection Technique.
1. Cooperative Detection: In this method group of CRâ&#x20AC;&#x2122;s share sensing information so as to get a more efficient result.
In this process group of secondary user (Su) collect the information regarding channel occupancy and maintain
this information into spectrum map represented by bit-vector. Su periodically transmit it to the Central
Coordinator as part of control message. Central coordinator takes bitwise-OR of spectrum maps, to determine the
set of UHF channels available at all of the nodes. After that Coordinator select the best available channel and
broadcast it back to Su. This technique exploits the spatial diversity intrinsic to a multi-user network. It can be
accomplished in a centralized or distributed fashion.[5]
There are broadly three approaches for cooperative spectrum sensing:
16

a) Centralized approach: In this approach to cognitive radio cooperative spectrum sensing, there is a node called
fusion center (FC) or central processor controls within the network that collects the sensing information from all
the sense nodes or radios within the network. It then analyses the information and determines the frequencies
which can be used.[8]
b) Distributed approach: In this approach distributed approach for cognitive radio cooperative spectrum sensing,
no one node act as fusion center (FC) or central processor controls. Instead communication exists between the
different nodes and they are able to share sense information. However this approach need individual radios to
have a much higher level of autonomy, and possibly setting themselves up as an ad-hoc network.[9]
c)

Relay-assisted cooperative: Besides centralized and distributed cooperative sensing, the third scheme is relayassisted cooperative sensing. Since both sensing channel and report channel are not perfect, a CR user observing a
weak sensing channel and a strong report channel and a CR user with a strong sensing channel and a weak report
channel, for example, can complement and cooperate with each other to improve the performance of cooperative
sensing. When the sensing results need to be forwarded by multiple hops to reach the intended receive node, all
the intermediate hops are relays. Thus, if both centralized and distributed structures are one-hop cooperative
sensing, the relay-assisted structure can be considered as multi-hop cooperative sensing.

2. Non-Cooperative Detection: In this Detection technique Individual radios act locally and autonomously to carry
out their own spectrum occupancy measurements and analysis. [10]
There are broadly three approaches for cooperative spectrum sensing:

a) Blind Sensing: In this approach to cognitive radio cooperative spectrum sensing, there is a node called fusion
center (FC) controls within the network that collects the sensing information from all the sense nodes or radios
within the network. It then analyses the information and determines the frequencies which can be used.
1. Energy Detector based sensing: If a receiver cannot gather sufficient information about the primary userâ&#x20AC;&#x2122;s
signal, such as in the case that only the power of random Gaussian noise is known to the receiver, the optimal
detector is an energy detector. Energy detection is simple and can be implemented efficiently by using a Fast
Fourier Transform (FFT) algorithm. However, there are some drawbacks for energy detection. First, the decision
threshold is subject to changing signal-to-noise ratios (SNRâ&#x20AC;&#x2122;s). Second, it can not distinguish interference from a
user signal. And third, it is not effective for signals whose signal power has been spread over a wideband [17].
2. Eigen value based Sensing: The eigenvalue of the covariance matrix of the received signal can also serve
the purpose of primary detection. With the help of random matrix theory, the ratio of the maximum eigenvalue to
the minimum eigenvalue is quantized, and one of the quantized values is chosen as detection threshold is. [3,4]
3. Covariance based Sensing: As a matter of fact statistical covariance matrices of the received signal and that
of noise are normally different. By utilizing this difference we can differentiate the desired signal component from
background noise.[6,7]
ď&#x201A;§ Antenna Correlation Based Sensing: Antenna correlation based detector by extending the covariance based
detector from time domain to space domain via exploiting the correlation among antennas. Obtaining threshold
17

level to achieve required probability of false alarm due to the approximate in the derivation which is helpful in
order to sense spectrum.
b) Signal Specific: This sensing technique requires prior knowledge of Primary User (PU) signal.
1. Waveform based Sensing: This method is only applicable to systems with known signal patterns which
could be preambles, midambles, regularly transmitted pilot patterns, spreading sequences and etc. It is termed as
waveform-based sensing or coherent sensing. It is shown that waveform based sensing outperforms energy
detector based sensing in reliability and convergence time. Furthermore, it is shown that the performance of the
sensing algorithm increases as the length of the known signal pattern increases. [11-13].
2. Transmitter Based Sensing: Here, the cognitive radio attempts to discern areas of used or unused spectrum
by determining if a primary user is transmitting in its vicinity. This approach is predicated on detecting not the
strongest transmitted signal from a primary user, but the weakest. The idea is that the weakest signal producing
primary transmitter would ideally be the one furthest away from the cognitive radio, but still susceptible to RF
interference from the radio. The basic hypothesis for transmitter detection as:

Here, x(t) is the signal received by the cognitive radio, s(t) is the transmitted signal of the primary user, n(t) is
all white Gaussian noise (AWGN) and h is the amplitude gain of the channel. H0 is a null hypothesis, which
states that there is no licensed (primary) user signal in a certain band. H1 is an alternative hypothesis, which
indicates that there exists some licensed user signal. The three main detection techniques which rely on this
hypothesis for transmitter detection are described below.
 Energy Sensing: If a receiver cannot gather sufficient information about the primary user’s signal, such as in
the case that only the power of random Gaussian noise is known to the receiver, the optimal detector is an
energy detector. Energy detection is simple and can be implemented efficiently by using a Fast Fourier
Transform (FFT) algorithm. However, there are some drawbacks for energy detection. First, the decision
threshold is subject to changing signal-to-noise ratios (SNR’s). Second, it can not distinguish interference from
a user signal. And third, it is not effective for signals whose signal power has been spread over a wideband.
 Matched Filter Sensing: The matched filter works by correlating a known signal, or template, with an
unknown signal to detect the presence of the template in the unknown signal. Because most wireless network
systems have pilots, preambles, synchronization word, or spreading codes, these can be used for coherent
(matched filter) detection. A big plus in favor of the matched filter is that it requires less time to achieve a high
processing gain due to coherency. The main shortcoming of the matched filter is that it requires a priori
knowledge of the primary user signal which in a real world situation may not be available.
 Cyclostationary Based Sensing: Because modulated signals are coupled with sine wave carriers, repeating
spreading code sequences, or cyclic prefixes all of which have a built-in periodicity, their mean and
autocorrelation exhibit periodicity which is characterized as being cyclostationary. Noise, on the other hand, is
a wide-sense stationary signal with no correlation. Using a spectral correlation function, it is possible to
differentiate noise energy from modulated signal energy and thereby detect if a primary user is present.
Cyclostationary feature detection is a promising option especially in cases where energy detection, described
next, is not so effective. However, cyclostationary detection requires a large computational capacity and
significantly long observation times.
3. Radio Identification Based Sensing: This method veers from the typical study of interference which is
usually transmitter-centric. Typically, a transmitter controls its interference by regulating its output transmission
power, its out-of-band emissions, based on its location with respect to other users. Cognitive radio identificationbased detection concentrates on measuring interference at the receiver. The FCC introduced a new model of
measuring interference referred to as interference temperature. The model accounts for cumulative RF energy
from multiple transmissions and sets a maximum cap on their aggregate level. As long as the transmissions of
cognitive radio users do not exceed this limit, they can use a particular spectrum band. The major hurdle with this
method is that unless the cognitive user is aware of the precise location of the nearby primary user, interference
18

cannot be measured with this method. An even bigger problem associated with this method is that it still allows an
unlicensed cognitive radio user to deprive a licensee (primary user) access to his licensed spectrum. This situation
can occur if a cognitive radio transmits at high power levels while existing primary users of the channel are quite
far away from a receiver and are transmitting at a lower power level.
III.

PROPOSED ANALYSIS

As we know cognitive radio network is future technology and very few works are done in this field so far.
Through this paper we have tried to give an idea based on spectrum sensing techniques in order to utilizing the
spectrum band. As we have discussed the Spectrum sensing techniques with tree diagram in Figure 3. In cooperative
all the radio nodes are working together for spectrum sensing while for Non-cooperative each and every radio nodes
are working individually. So using figure 5, we can say if we have High (Signal to Noise Ratio) SNR the probability
of primary user detection is approximate same for both the detection scheme (Cooperative as well as Noncooperative) But low value of SNR, performance of non-cooperative as well as cooperative detection is decreasing
while cooperative detection is better as compare to Non-cooperative detection.

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